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Multiple linear regression determined relationships among variables, and independent t tests were used to compare centralized and conventional foodservice systems.
Multivariate linear regression determined the impact of health care resource and economic variables on our outcome variable, and TB mortality rates.
For aggressive NHL, differences in 3-year progression-free survival (PFS /event-free survival (EFS) were high correlated with differences in 5-year OS { rs of 0.90 [95% confidence interval (CI) 0.73–0.96]} and linear regression determined that a 10% imPFS /event-freeyear EFsurvival would prEFSct for a 7% ± 1% improvement in 5-year OS.
Two models of linear regression determined the influence of independent variables such as age, gender, and hours in direct patient care on job satisfaction and each of the three professional autonomy variables (forced entry with cut-off scores of p < 0.05 for inclusion and p > 0.10 for exclusion).
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The minimization process is made under the stacked regression assumption that each of the two component models can be fitted alone, and then a linear regression determines the weight of each of them.
Our approach is to combine the flows of human mobility estimated from the two models under a stacked regression procedure [42] in which each model is fitted alone, and then a linear regression determines the weight of each of them.
Linear mixed-model regression determined the effects of the pollutants on systemic IL-6 and fibrinogen.
A multiple linear regression model determined those factors that influence each marker level.
The best-fit linear regression was determined for each data set.
The predictions of multiple linear regression models determined based on the factorial design of experiments are excellent.
The slopes of the line by linear regression were determined.
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